SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images

TL;DR

This paper introduces SRA, a histopathology-specific image augmentation method, integrated with MoCo v3, which consistently improves feature embedding and downstream task performance in self-supervised learning for histopathological images.

Contribution

The paper presents a novel stain reconstruction augmentation (SRA) tailored for histopathology, enhancing self-supervised contrastive learning when combined with MoCo v3.

Findings

SRA-MoCo v3 outperforms standard MoCo v3 in various tasks.

SRA-MoCo v3 achieves comparable or better results than models trained on larger datasets.

The proposed augmentation improves feature embedding quality.

Abstract

Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised learning, as it generates variations in image samples. However, traditional image augmentation techniques often overlook the unique characteristics of histopathological images. In this paper, we propose a new histopathology-specific image augmentation method called stain reconstruction augmentation (SRA). We integrate our SRA with MoCo v3, a leading model in self-supervised contrastive learning, along with our additional contrastive loss terms, and call the new model SRA-MoCo v3. We demonstrate that our SRA-MoCo v3 always outperforms the standard MoCo v3 across various downstream tasks and achieves comparable or superior performance to other foundation models pre-trained on significantly larger histopathology datasets.